Purpose

Time of flight (ToF) imaging is a promising emerging technology for the purposes of crop identification. This paper aim to presents localization system for identifying and localizing asparagus in the field based on point clouds from ToF imaging. Since the semantics are not included in the point cloud, it contains the geometric information of other objects such as stones and weeds other than asparagus spears. An approach is required for extracting the spear information so that a robotic system can be used for harvesting.

Design/methodology/approach

A real-time convolutional neural network (CNN)-based method is used for filtering the point cloud generated by a ToF camera, allowing subsequent processing methods to operate over smaller and more information-dense data sets, resulting in reduced processing time. The segmented point cloud can then be split into clusters of points representing each individual spear. Geometric filters are developed to eliminate the non-asparagus points in each cluster so that each spear can be modelled and localized. The spear information can then be used for harvesting decisions.

Findings

The localization system is integrated into a robotic harvesting prototype system. Several field trials have been conducted with satisfactory performance. The identification of a spear from the point cloud is the key to successful localization. Segmentation and clustering points into individual spears are two major failures for future improvements.

Originality/value

Most crop localizations in agricultural robotic applications using ToF imaging technology are implemented in a very controlled environment, such as a greenhouse. The target crop and the robotic system are stationary during the localization process. The novel proposed method for asparagus localization has been tested in outdoor farms and integrated with a robotic harvesting platform. Asparagus detection and localization are achieved in real time on a continuously moving robotic platform in a cluttered and unstructured environment.

中文翻译：

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使用飞行时间成像进行机器人收割芦笋矛的定位

目的

飞行时间 (ToF) 成像是一种用于农作物识别的有前途的新兴技术。本文旨在提出基于 ToF 成像点云的现场识别和定位芦笋的定位系统。由于点云中不包含语义，因此它包含除芦笋矛之外的其他物体（例如石头和杂草）的几何信息。需要一种方法来提取矛信息，以便机器人系统可以用于收割。

设计/方法论/途径

基于实时卷积神经网络（CNN）的方法用于过滤ToF相机生成的点云，允许后续处理方法在更小、信息更密集的数据集上运行，从而减少处理时间。然后，分割的点云可以分成代表每个单独矛的点簇。开发几何过滤器是为了消除每个簇中的非芦笋点，以便可以对每个矛进行建模和本地化。然后，矛信息可用于收获决策。

发现

定位系统集成到机器人收割原型系统中。已进行多次现场试验，效果令人满意。从点云中识别矛是成功定位的关键。将点分割和聚集到单个矛中是未来改进的两个主要失败。

原创性/价值

使用 ToF 成像技术的农业机器人应用中的大多数作物定位都是在严格受控的环境中实现的，例如温室。目标作物和机器人系统在定位过程中保持静止。提出的新颖的芦笋定位方法已在室外农场进行了测试，并与机器人收割平台集成。芦笋检测和定位是在杂乱且非结构化的环境中在连续移动的机器人平台上实时实现的。